""" Project: enhanced_cs.NE_2508.07077v1_Enhancing_Decision_Space_Diversity_in_Multi_Object Type: optimization Description: Enhanced AI project based on cs.NE_2508.07077v1_Enhancing-Decision-Space-Diversity-in-Multi-Object with content analysis. """

import logging import os import sys import time from typing import Dict, List, Tuple

Set up logging

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(name)

Define constants

PROJECT_NAME = "Enhanced Decision Space Diversity" PROJECT_VERSION = "1.0" PROJECT_AUTHOR = "Gustavo V. Nascimento, Ivan R. Meneghini, Valéria Santos, Eduardo Luz, Gladston Moreira"

Define configuration

class Configuration: def init(self): self.settings = { "velocity_threshold": 0.5, "flow_theory_threshold": 0.8, "max_iterations": 100, "population_size": 100, "mutation_rate": 0.1, "crossover_rate": 0.5 }

def get(self, key):
    return self.settings.get(key)

def set(self, key, value):
    self.settings[key] = value

Define exception classes

class OptimizationError(Exception): pass

class InvalidConfigurationError(OptimizationError): pass

class InvalidInputError(OptimizationError): pass

Define data structures/models

class Solution: def init(self, x: float, y: float): self.x = x self.y = y

def __repr__(self):
    return f"Solution(x={self.x}, y={self.y})"

class Population: def init(self, size: int): self.solutions = [Solution(0, 0) for _ in range(size)]

def __repr__(self):
    return f"Population(size={len(self.solutions)})"

Define validation functions

def validate_configuration(config: Configuration): if config.get("velocity_threshold") < 0 or config.get("velocity_threshold") > 1: raise InvalidConfigurationError("Velocity threshold must be between 0 and 1") if config.get("flow_theory_threshold") < 0 or config.get("flow_theory_threshold") > 1: raise InvalidConfigurationError("Flow theory threshold must be between 0 and 1") if config.get("max_iterations") <= 0: raise InvalidConfigurationError("Max iterations must be greater than 0") if config.get("population_size") <= 0: raise InvalidConfigurationError("Population size must be greater than 0") if config.get("mutation_rate") < 0 or config.get("mutation_rate") > 1: raise InvalidConfigurationError("Mutation rate must be between 0 and 1") if config.get("crossover_rate") < 0 or config.get("crossover_rate") > 1: raise InvalidConfigurationError("Crossover rate must be between 0 and 1")

def validate_input(solution: Solution): if solution.x < 0 or solution.x > 1: raise InvalidInputError("X coordinate must be between 0 and 1") if solution.y < 0 or solution.y > 1: raise InvalidInputError("Y coordinate must be between 0 and 1")

Define utility methods

def calculate_velocity(solution: Solution, config: Configuration): return solution.x * config.get("velocity_threshold")

def calculate_flow_theory(solution: Solution, config: Configuration): return solution.y * config.get("flow_theory_threshold")

def mutate_solution(solution: Solution, config: Configuration): if random.random() < config.get("mutation_rate"): solution.x += random.uniform(-0.1, 0.1) solution.y += random.uniform(-0.1, 0.1) return solution

def crossover_solution(solution1: Solution, solution2: Solution, config: Configuration): if random.random() < config.get("crossover_rate"): solution1.x = (solution1.x + solution2.x) / 2 solution1.y = (solution1.y + solution2.y) / 2 return solution1

Define optimization algorithm

class OptimizationAlgorithm: def init(self, config: Configuration): self.config = config self.population = Population(config.get("population_size"))

def run(self):
    try:
        validate_configuration(self.config)
        for iteration in range(self.config.get("max_iterations")):
            logger.info(f"Iteration {iteration+1} of {self.config.get('max_iterations')}")
            for solution in self.population.solutions:
                validate_input(solution)
                velocity = calculate_velocity(solution, self.config)
                flow_theory = calculate_flow_theory(solution, self.config)
                logger.info(f"Solution {solution} has velocity {velocity} and flow theory {flow_theory}")
                mutated_solution = mutate_solution(solution, self.config)
                logger.info(f"Mutated solution {mutated_solution}")
                crossover_solution(mutated_solution, solution, self.config)
                logger.info(f"Crossover solution {mutated_solution}")
            logger.info(f"Population size: {len(self.population.solutions)}")
    except OptimizationError as e:
        logger.error(f"Optimization error: {e}")
    except Exception as e:
        logger.error(f"Unexpected error: {e}")

Define main class

class Main: def init(self): self.config = Configuration() self.algorithm = OptimizationAlgorithm(self.config)

def run(self):
    self.algorithm.run()

if name == "main": main = Main() main.run()